Method to locate defects in e-coat

ABSTRACT

A method of locating a defect in an e-coat on a surface can include acquiring an image of the surface. A correction coefficient can be applied to the image to form an adjusted image. The correction coefficient can relate pixel values of the image to a calibration value. The adjusted image can be separated into a spectral component which can be modified by a block average determination to create a modified spectral component. The spectral components can be compared with the modified spectral components to form a difference image. The difference image can be dilated and eroded. A region of interest can be identified from an image region using a blob detection. The defect can be classified as a defect type. The defect can be repaired or a coding parameter can be altered based on the defect.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.63/223,676, filed on Jul. 20, 2021. The entire disclosure of the aboveapplication is incorporated herein by reference.

FIELD

The present technology relates to vision systems and, more particularly,to system and method for locating defects in paint.

INTRODUCTION

This section provides background information related to the presentdisclosure which is not necessarily prior art.

The vehicle manufacturing process requires several stages, such asstamping, welding, painting or electrophoretic coating (e-coating), partforging, and part assembly. Some of the vehicle manufacturing stages areautomated, while others are done manually. One of the steps that ispredominately done manually is painting or e-coating. E-coating is theprocess of depositing certain particles onto the surface of a piece ofmetal. This is done by placing coating materials, such as resins,pigments and additives, in a water bath to create a solution. The partsof the car are then immersed in the solution and an electrical currentis passed through the bath using the parts as an electrode. The e-coatcan protect the surface, edges, and cavities of the car body fromcorrosion as well as provide a base for applying subsequent layers ofpaint.

Undesirably, because e-coating is done in a manufacturing environment,the process can often yield defects that can occur on the surface of thepart. These defect and imperfections can be caused by dirt, craters,fibers, glue, sealer, fingerprints, mapping, condensation, or othercontaminants in the paint, e-coating, air, or present on the part. Thesedefects in the finish of the surface can result in the paint ore-coating not adhering to the surface properly, an increase in thefrequency at which paint or e-coating chipping, a rougher surface inwhich dirt can easily collect, and an overall unappealing aesthetic.

Accordingly, there is a need for a method for locating defects ine-coats.

SUMMARY

In concordance with the instant disclosure, methods and systems forlocating defects in e-coats, have surprisingly been discovered.

In one embodiment, a method of locating a defect in an e-coat on asurface can include acquiring an image of the surface. A correctioncoefficient can be applied to the image to form an adjusted image. Thecorrection coefficient can relate pixel values of the image to acalibration value. The adjusted image can be separated into a spectralcomponent. The spectral component can be modified by a block averagedetermination to create a modified spectral component. The spectralcomponents can be compared with the modified spectral components to forma difference image. The difference image can be dilated and eroded. Aregion of interest can be identified from one or more image regionsusing a blob detection threshold value to locate the defect. The defectcan be classified as a defect type. The defect can be repaired, or acoding process parameter can be altered based on the identification ofthe defect.

In another exemplary embodiment, a method for locating defects ine-coats can be described as hereinbelow. An image can be acquired usinga method similar to a current clearcoat system. A vehicle can move on aconveyor and the vehicle's position can be tracked with the precision ofat least 2 mm resolution. This can be achieved by using a rotaryencoder, laser, or other similar tracking device. An X-position can becalculated with respect to a start position. The start position can beestablished by using a vehicle tripping photo eye.

A trigger table can define the X-positions while a camera takes imagesof the vehicle's surface and the surface of the vehicle is illuminatedby a plurality of lights. The typical spacing of the vehicle'sX-position travel between subsequent frames can be about 3-8 mm.

Images can be taken using a color camera. As a non-limiting example, thetype of camera can include a JAI Go™ series color camera (e.g., theGo-5100C-PGE) with a Bayer pixel format. An algorithm (such asinterpolation) can be applied to convert the Bayer pixel image format toa color image format having Red Green Blue (RGB) values for each pixel.If the Red, the Green, and the Blue values each have 8 bits, then therange in values for each can be from 0-255.

A color calibration plate can be used to find color correctioncoefficients to make RGB values of each pixel closer to an expected RGBvalues of color calibration plate. An XRite™ passport color calibrationplate can be used to find three multiplier values for red, green, andblue. The color calibration plate can minimize the error between thecolors after the multipliers are applied verses the expected RGB valuesof the color squares on the calibration plate. Images captured by thecamera can have RGB values adjusted by these multipliers. Frames at agiven X-position can be associated with a frame id number.

At a given X-position, the surface area and light illumination can besimilar from vehicle to vehicle. Masks to crop areas of the image can bedefined for a given X-position (likewise for a given frame id number).Masked and cropped areas can be ignored for image processing purposes.

Images can be processed on a GPU to take advantage of parallelprocessing on the GPU. The objective of image processing for an imageframe can include finding where defects are present on e-coat surface.Non-limiting examples of the defects on e-coat can include glue, sealer,fingerprints, mapping, fibers, dirt, craters, condensation, etc.

An adjusted color image can be separated by RGB to three monochromeimages: red, green, and blue. A fourth monochrome grey image can becreated by assigning the maximum value of the red, green, or blue valueat the pixel to the grey image pixel.

The grey image can be processed by a BlockAverage process. TheBlockAverage process can input a block width, a block height, and apercentile limit. The image can then be divided into blocks having thespecified block width and block height. The pixels in each block can besorted by intensity into a histogram of pixel intensity values (for8-bit images, this histogram can have buckets that range from 0-255).The histogram can be used to calculate the percentile of the pixelintensities. Any pixels below the percentile limit intensity percentilecan be discarded, and pixels above (1—the percentile limit intensitypercentile) can be discarded. So, for example, if the percentile limitis 0.1, then the pixel values below the tenth percentile intensity levelcan be discarded, and pixels above the 90th percentile intensity valuecan be discarded. All the remaining pixel values that have not beendiscarded, as recorded in the histogram, can then be used to calculatean average pixel intensity. Once the average pixel intensity of theremaining pixels is calculated, the entire block of pixels can bereplaced by this average pixel intensity. In this way, a second layeredaverage image can be created from the block averages.

A difference from background image can be created by iterating over eachpixel in the background intensity image to find the difference betweenthe background image pixel and the grey image pixel at the same X,Yimage coordinate to create a difference image. If the difference at thepixel is less than a threshold value, then the difference image pixelcan be 0. Otherwise, the difference image pixel will be the same as theabsolute value of the difference. The threshold can be set to beadjusted according to changes in the average intensity value. Apredefined table of threshold values can be used to map a thresholdvalue to each possible background intensity value. So, for example, thedifference threshold may be relatively low at 10 if the average value isless than 40, however it can increase to 15 when the value is higherthan 80 and go as high as 35 if the value is greater than 220.

The difference image can then be eroded to remove noise. For example,the difference image can be eroded by 1 pixel, and then eroded by 1pixel again, and then eroded by 1 pixel again to produce an erodedimage. Then, a new final eroded image can be created by merging theeroded image with the difference image according to the followingmethod. First, zero out the pixel value in the difference image.However, if the pixel value is greater than some threshold (for example40) or the pixel still exists in the eroded image the pixel value shouldnot be zeroed out. Desirably, the final eroded image can have less noisypixels.

The final eroded image can then be processed by a series of dilationsand erosions to create connected regions. For example, the image can beprocessed by a sequence of 3 dilations having radius of 4, followed by 3erosions also having radius of 4 to produce a regions image.

Next, the defect regions of interest in the region image can be located.For each region, pixels can be zeroed out unless they are greater than 0in the final eroded image and greater than 0 in the regions image. Thesesaved pixels can be used to define the final regions. Any regions havingless than 5 saved pixels can be discarded. A color version of the defectis created by taking the color values from adjusted color image usingthe saved pixels' X and Y values. A bounding box can be calculated toencompass the saved pixels. The saved pixels and bounding box can beused to create a defect region of interest for each defect region found.

The defect region of interest can have classification informationassociated to it beyond just the bounding box and saved pixels.Information can include color information because different e-coatdefects can have different color. Color can also be used to filter outnoise because e-coat color is usually the same for the entire vehicle.The size of the region can also be used to filter out noise. Once defectregions of interest are extracted from an image, they can be associatedwith the frame ID and the X-position.

Subsequently, as is already done for clear coat systems and described inother patent applications, the defect regions of interest that are foundcan be passed through a classifier to be sorted into various defecttypes or to be classified as noise and rejected. The classifier caninclude an SVM, or a deep learning coevolution neural network, such as avariation of AlexNet™. The features supplied to the classifier caninclude color as well as features currently used in clear coat systems,such as shape, position, size, and probability of noise being found on acamera frame at a given X-position.

After a defect region of interest has been passed through aclassification step, a hit testing approach can be used to find thelocation of the defect on the surface of the vehicle. The hit test canuse the calibrated position of the camera and simulates the vehicleusing a 3D CAD model of the vehicle located at the simulated X-positionof the vehicle when the image was taken. Next, hit testing can be usedto trace a ray from the center pixel of the defect region of interest incomputer simulation until it intersects with a point on the 3D vehiclemodel. When hit testing is successful, the expected surface normal canbe also available from the 3D model. Hit testing for the region can alsobe repeated at each corner of the region.

Because images are taken frequently as the vehicle is scanned (which canbe approximately every 4 mm of travel) multiple images of the samedefect can be captured, which can result in multiple defect regions ofinterest for the same defect. When defect regions of interest arerepeated in more than one image frame, they still can have the same hittest location on the 3D vehicle (which can be close within somethreshold specified in mm and commonly less than 2 mm). A clusteringstep can take all the defect regions of interest and build a clusterfrom defect regions of interest located close to each other on thevehicle's surface based on comparisons of the hit test locations. Defectregions of interest that cluster together can be assumed to be the samedefect and can be studied as a group to find the best classification,size, and location estimate of the defect.

Once a defect's type and location are found from a plurality of thedefect regions of interest, the information can be used to alert repairoperators to the defect, to store quality data used to measure andimprove the paint shop processes, and/or to inform an automatic repairsystem (such as a robot) about a defect's location, size, and type.

Color variations in images of e-coat surfaces can be used as a signal todetect and classify defects. The calculate color distance process canreceive the grey image and also the color adjusted red, green, and blueimages for comparison with the background (layered) image. The greyintensities of pixels in the background image can be converted to abackground integer value (between 0 and 255 for 8-bit grey images) usinga rounding or truncation operation. Each pixel value in the grey imagecan be subtracted from the corresponding (same X,Y coordinate) pixelvalue in the background image. If the absolute value of this backgrounddifference is less than a threshold (for example 2) then the pixellocation can be considered part of the e-coat background. Red, Green,and Blue intensity values of pixels considered part of the e-coatbackground can be sorted according to the background integer value atthe pixel location.

Next, the average Red, Green, and Blue intensity values for eachbackground integer value can be calculated by averaging the red, green,and blue pixel values at each background integer. Finally, a colordistance image can be created by iterating over each pixel in thebackground image, converting the background pixel value to an integervalue, looking up the average RGB intensity for that background value,and then finding the difference between the average Red, Green, and Blueintensity values and the adjusted color image's Red, Green, and Bluevalues respectively.

The Red Green and Blue channel differences can be used to generate acolor image that signals where color is different from the standarde-coat color. The absolute value of the vector magnitude of the RGBdifferences at each pixel can also be used to create a monochrome colorvariation image that highlights where color is different from theexpected e-coat color. This monochrome color variation image can be usedto filter out noise regions and/or to identify defect type. For example,it is possible to iterate over the non-zero pixels in a defect region ofinterest and tally the 5 highest value corresponding (X,Y coordinate)monochrome color variation image pixels. If this tally is lower than athreshold the region can be discarded as noise. Similarly, thecorresponding red, green, and blue color differences can be used ascomponents of feature vectors for a classifier for sorting out thedefect type because different types of defects on e-coat can havedifferent colors.

Further areas of applicability will become apparent from the descriptionprovided herein. The description and specific examples in this summaryare intended for purposes of illustration only and are not intended tolimit the scope of the present disclosure.

DRAWINGS

The drawings described herein are for illustrative purposes only ofselected embodiments and not all possible implementations, and are notintended to limit the scope of the present disclosure.

FIG. 1 is a schematic side elevational view of a system for locating adefect in an e-coat on a surface, which can be used to implement amethod to locate defects in e-coat, as shown in FIGS. 2A and 2B;

FIGS. 2A and 2B form a flow diagram illustrating the method to locatedefects in e-coat;

FIG. 3 is a flow diagram illustrating the step of acquiring the image;

FIG. 4 is a flow diagram illustrating the step of applying thecorrection coefficient to the image to form the adjusted image;

FIG. 5 is a flow diagram illustrating the step of separating a spectralcomponent from the adjusted image;

FIG. 6 is a flow diagram illustrating the step of modifying the spectralcomponent to create a modified spectral component;

FIG. 7 is a flow diagram illustrating the step of comparing the spectralcomponent with the modified spectral component to form a differenceimage;

FIG. 8 is a flow diagram illustrating the step of identifying a regionof interest using blob detection;

FIG. 9 is a flow diagram illustrating the step of repairing the defect;and

FIG. 10 is a flow diagram illustrating the step of changing a parameterof a coding process.

DETAILED DESCRIPTION

The following description of technology is merely exemplary in nature ofthe subject matter, manufacture and use of one or more inventions, andis not intended to limit the scope, application, or uses of any specificinvention claimed in this application or in such other applications asmay be filed claiming priority to this application, or patents issuingtherefrom. Regarding methods disclosed, the order of the steps presentedis exemplary in nature, and thus, the order of the steps can bedifferent in various embodiments, including where certain steps can besimultaneously performed, unless expressly stated otherwise. “A” and“an” as used herein indicate “at least one” of the item is present; aplurality of such items may be present, when possible. Except whereotherwise expressly indicated, all numerical quantities in thisdescription are to be understood as modified by the word “about” and allgeometric and spatial descriptors are to be understood as modified bythe word “substantially” in describing the broadest scope of thetechnology. “About” when applied to numerical values indicates that thecalculation or the measurement allows some slight imprecision in thevalue (with some approach to exactness in the value; approximately orreasonably close to the value; nearly). If, for some reason, theimprecision provided by “about” and/or “substantially” is not otherwiseunderstood in the art with this ordinary meaning, then “about” and/or“substantially” as used herein indicates at least variations that mayarise from ordinary methods of measuring or using such parameters.

Although the open-ended term “comprising,” as a synonym ofnon-restrictive terms such as including, containing, or having, is usedherein to describe and claim embodiments of the present technology,embodiments may alternatively be described using more limiting termssuch as “consisting of” or “consisting essentially of.” Thus, for anygiven embodiment reciting materials, components, or process steps, thepresent technology also specifically includes embodiments consisting of,or consisting essentially of, such materials, components, or processsteps excluding additional materials, components or processes (forconsisting of) and excluding additional materials, components orprocesses affecting the significant properties of the embodiment (forconsisting essentially of), even though such additional materials,components or processes are not explicitly recited in this application.For example, recitation of a composition or process reciting elements A,B and C specifically envisions embodiments consisting of, and consistingessentially of, A, B and C, excluding an element D that may be recitedin the art, even though element D is not explicitly described as beingexcluded herein.

As referred to herein, disclosures of ranges are, unless specifiedotherwise, inclusive of endpoints and include all distinct values andfurther divided ranges within the entire range. Thus, for example, arange of “from A to B” or “from about A to about B” is inclusive of Aand of B. Disclosure of values and ranges of values for specificparameters (such as amounts, weight percentages, etc.) are not exclusiveof other values and ranges of values useful herein. It is envisionedthat two or more specific exemplified values for a given parameter maydefine endpoints for a range of values that may be claimed for theparameter. For example, if Parameter X is exemplified herein to havevalue A and also exemplified to have value Z, it is envisioned thatParameter X may have a range of values from about A to about Z.Similarly, it is envisioned that disclosure of two or more ranges ofvalues for a parameter (whether such ranges are nested, overlapping ordistinct) subsume all possible combination of ranges for the value thatmight be claimed using endpoints of the disclosed ranges. For example,if Parameter X is exemplified herein to have values in the range of1-10, or 2-9, or 3-8, it is also envisioned that Parameter X may haveother ranges of values including 1-9, 1-8, 1-3, 1-2, 2-10, 2-8, 2-3,3-10, 3-9, and so on.

When an element or layer is referred to as being “on,” “engaged to,”“connected to,” or “coupled to” another element or layer, it may bedirectly on, engaged, connected or coupled to the other element orlayer, or intervening elements or layers may be present. In contrast,when an element is referred to as being “directly on,” “directly engagedto,” “directly connected to” or “directly coupled to” another element orlayer, there may be no intervening elements or layers present. Otherwords used to describe the relationship between elements should beinterpreted in a like fashion (e.g., “between” versus “directlybetween,” “adjacent” versus “directly adjacent,” etc.). As used herein,the term “and/or” includes any and all combinations of one or more ofthe associated listed items.

Although the terms first, second, third, etc. may be used herein todescribe various elements, components, regions, layers and/or sections,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be only used to distinguishone element, component, region, layer or section from another region,layer or section. Terms such as “first,” “second,” and other numericalterms when used herein do not imply a sequence or order unless clearlyindicated by the context. Thus, a first element, component, region,layer or section discussed below could be termed a second element,component, region, layer or section without departing from the teachingsof the example embodiments.

Spatially relative terms, such as “inner,” “outer,” “beneath,” “below,”“lower,” “above,” “upper,” and the like, may be used herein for ease ofdescription to describe one element or feature's relationship to anotherelement(s) or feature(s) as illustrated in the figures. Spatiallyrelative terms may be intended to encompass different orientations ofthe device in use or operation in addition to the orientation depictedin the figures. For example, if the device in the figures is turnedover, elements described as “below” or “beneath” other elements orfeatures would then be oriented “above” the other elements or features.Thus, the example term “below” can encompass both an orientation ofabove and below. The device may be otherwise oriented (rotated 90degrees or at other orientations) and the spatially relative descriptorsused herein interpreted accordingly.

The present technology relates to a method 100 to locate defects ine-coat on a surface, shown in FIG. 1 and shown generally in theaccompanying figures. Advantageously, the method 100 can distinguishbetween prospective defects that are real defects and prospectivedefects that are noise.

As shown in FIGS. 2A-2B, the method to locate a defect in an e-coat on asurface can include a step 102 of acquiring an image of the surface.With reference to FIG. 3 , the step 102 of acquiring an image of thesurface can include a step 104 of placing the surface on a conveyor at afirst location. The first location can be established by the surfacetripping a photo eye. The surface can be moved along the conveyor to asecond location space away from the first location in a step 106. Thesecond location can be selected from a predetermined trigger table. In astep 108, the second position can be tracked with respect to the firstposition using a tracking device. The tracking device can be a rotaryencoder or a laser. One of ordinary skill in the art can select othersuitable tracking devices within the scope of the present disclosure.The distance between the first location and the second location can becalculated in a step 110. Advantageously, calculating the distancebetween the first location and the second location can allow for thedefect to be located on the surface quickly for repair. As anon-limiting example, the distance between the first location and thesecond location can be between about 2 millimeters and about 8millimeters. More specifically, the distance between the first locationand the second location can be about 4 millimeters. Advantageously, adistance within the above stated range can allow for thorough inspectionof the surface. One of ordinary skill in the art can select othersuitable distance between the first location and the second locationwithin the scope of the present disclosure.

With continued reference to FIG. 3 , the surface can be illuminated witha light in a step 112. In a step 114, the image of the surface can becaptured at the second location. The image taken at the second locationcan be categorized with an identification number in a step 116.Desirably, the identification number can allow for the defect to bephysically located on the surface after it has been discovered andclassified. The means for acquiring the image of the surface can be acolor camera. The color camera can have a Bayer pixel format.Advantageously, the Bayer pixel format can allow for easier pixelseparation and better detection. More specifically, the camera can be aJAI Go series color camera. One of ordinary skill in the art can selectother suitable camera for acquiring an image within the scope of thepresent disclosure.

As shown in FIGS. 2A, 2B, and 4 , the method 100 to locate a defect inan e-coat on a surface can include a step 120 of applying a correctioncoefficient to the image to form an adjusted image, the correctioncoefficient relating pixel values of the image to a calibration value.With reference to FIG. 4 , the step 120 of applying a correctioncoefficient to the image to form an adjusted image can include a step122 of converting the image into color image format. The color imageformat can have a red value, a blue value, and a green value assigned toeach of a plurality of pixels of the image. In a step 124, thecorrection coefficient can be identified using a calibration plate. Thecalibration plate can act as a normalization step to color correct theimage. As a non-limiting example, the calibration plate can be an XRitepassport color calibration plate. One of ordinary skill in the art canselect other suitable calibration plates within the scope of the presentdisclosure. The correction coefficient can be applied to each of theplurality of pixels to form an adjusted image in a step 126.

In certain embodiments, the correction coefficient can include aplurality of correction coefficients. Each of the plurality ofcorrection coefficients can correspond to a color value. The red valuecan have a red correction coefficient. The blue value can have bluecorrection coefficient. The green value can have a green correctioncoefficient. Advantageously, the plurality of correction coefficientscan minimize the error between the colors of the adjusted image and theexpected red value, blue value, and green value of the calibrationplate. The plurality of correction coefficients can be applied to eachof the plurality of pixels to form an adjusted image.

As shown in FIGS. 2A, 2B, and 5 , the method 100 to locate a defect inan e-coat on a surface can include a step 140 of separating a spectralcomponent from the adjusted image at each of the plurality of pixels.With reference to FIG. 5 , the step 140 of separating a spectralcomponent from the adjusted image can include a step 142 of separatingthe adjusted image into multiple spectral components at each of theplurality of pixels. The multiple spectral components can include a step144 of separating the image into a red monochrome, whereby a red imageis created. In a step 146 the image can be separated into a bluemonochrome, whereby a blue image is created. In a step 148 the image canbe separated into a green monochrome, whereby a green image is created.One or more of the red image, the blue image, and the green image ateach of the plurality of pixels can be maximized to form a grey image ina step 150.

As shown in FIGS. 2A, 2B, and 6 , the method 100 to locate a defect inan e-coat on a surface can include a step 160 of modifying the spectralcomponents to create a modified spectral component, the modifyingincluding a block average determination. As a non-limiting example, thespectral components can be from the red image, the blue image, or thegreen image. More specifically, the spectral components can be from thegrey image. With reference to FIG. 6 , the step 160 of modifying thespectral component can include a step 162 of selecting a block region ofpixels. The block region can include a block height, a block width, anda percentile limit. In a step 164, the pixels of each block can besorted by pixel intensity. Where the pixels are sorted by pixelintensity, outlier pixels that fall outside the percentile limit can beidentified. The outlier pixels can be discarded in a step 166. Theremaining pixels can be averaged to determine an average pixel intensityin a step 168. In a step 170, the block region can be replaced with theaverage pixel intensity and form a background intensity image.

As a non-limiting example, the block average determination can modifythe spectral component by selecting a first block region can be selectedhaving the block width of about 400 pixels, the block height of about 5pixels, and the percentile limit of about 0.25. A percentile limit ofabout 0.25 can result in pixels with an intensity below 25% beingdiscarded as well as pixels with an intensity about 75% being discarded.The pixels in the first block region can be sorted by pixel intensityinto a histogram ranging in pixel intensity from 0-255. Advantageously,the histogram can be used to calculate the percentile of the pixelintensities. The pixels of the first block region that fall below thepercentile limit of 25% intensity and fall above the percentile limit of75% intensity can be discarded as outlier pixels. The block region canbe replaced with the average pixel intensity found in the histogram andform a first background intensity image.

As another non-limiting example, the block average determination withdifferent parameters can be used to modify the first backgroundintensity image. A second block region can be selected having the blockwidth of about 200 pixels, the block height of about 5 pixels, and thepercentile limit of about 0.1. A percentile limit of about 0.1 canresult in pixels with an intensity below 10% being discarded as well aspixels with an intensity about 90% being discarded. The pixels in thefirst block region can be sorted by pixel intensity into a histogram.Advantageously, the histogram can be used to calculate the percentile ofthe pixel intensities. The pixels of the first block region that fallbelow the percentile limit of 10% intensity and fall above thepercentile limit of 90% intensity can be discarded as outlier pixels.The block region can be replaced with the average pixel intensity foundin the histogram and form a second background intensity image.Desirably, the second block average determination can help to smooth theintensity differences in the average.

Advantageously, and as detailed in the above examples, the wide blockwidth and short block height can be useful in determining the backgroundintensity image where a light intensity is variable in the y-directionof the adjusted image which commonly occurs when using bright horizontallight to illuminate the surface.

As shown in FIGS. 2A, 2B, and 7 , the method 100 to locate a defect inan e-coat on a surface can include a step 180 of comparing the spectralcomponent with the modified spectral component to form a differenceimage. With reference to FIG. 7 , the step 180 of comparing the spectralcomponent with the modified spectral component to form a differenceimage can include a step 182 of comparing the background intensity imageand a grey image at each of the plurality of pixels to form a differenceimage is created. Where the difference at each of the plurality ofpixels between the background intensity image and the grey image is lessthan a minimum value, the difference image pixel can be zero. Where thedifference at each of the plurality of pixels between the backgroundintensity image and the grey image is equal to or greater than theminimum value, the difference image pixel can be the same as theabsolute value of the difference. The minimum value can be adjusted tochanges in the average intensity. As a non-limiting example, the minimumvalue can be relatively low at about 10 if the average intensity is lessthan about 40. As another non-limiting example, the minimum value mayincrease to about 15 when the average intensity is higher than about 80.The minimum can go as high as about 35 when the average intensity isgreater than about 220. One of ordinary skill in the art can selectother suitable minimum values within the scope of the presentdisclosure.

In a step 184, the difference image can be eroded to remove noise andform an eroded image. As a non-limiting example, the difference imagecan be eroded by about 1 pixel. The difference image can be eroded asecond time by another about 1 pixel. One of ordinary skill in the artcan select a suitable number of erosions within the scope of the presentdisclosure to remove noise from the difference image. Where thedifference image is eroded to remove noise, an eroded image is formed.In a step 186, the eroded image and the difference image can be merged,according to a baseline value, to form a final eroded image. Where thepixel average intensity is less than the baseline value, the pixel valueis zeroed out in the difference image. Where the pixel average intensityis equal to or greater than the baseline value or the pixel exists inthe eroded image, the pixel in the difference image and pixel in theeroded image are merged in the final eroded image. One of ordinary skillin the art can select a suitable baseline value within the scope of thepresent disclosure.

As shown in FIGS. 2A and B, the method 100 to locate a defect in ane-coat on a surface can include a step 200 of dilating and eroding thedifference image. Advantageously, dilating and eroding the differenceimage can allow for regions of pixel to connect and form connectedregions. The connected regions can allow for easier detection of theregion during blob detection. One of ordinary skill in the art canselect a suitable number of dilations and erosions within the scope ofthe present disclosure.

As shown in FIGS. 2A, 2B, and 8 , the method 100 to locate a defect inan e-coat on a surface can include a step 220 of identifying a region ofinterest from the one or more image regions using a blob detectionthreshold value to locate the defect. With reference to FIG. 8 , thestep 220 of identifying a region of interest from one or more imageregions can include a step 222 of blob detection. The step 222 of usingblob detection can include a step 224 of saving pixels with an intensityin the final eroded image greater than zero. In a step 226 the imageregions that reach a threshold value number of pixels can be bounded. Asa non-limiting example, the threshold value can be greater than 3pixels. More specifically, the threshold value can be five pixels. Oneof ordinary skill in the art can select a suitable threshold valuewithin the scope of the present disclosure. Regions that contain lesspixels than the threshold value can be discarded. The bounded regionscan be a defect region, shown in FIG. 9 . Advantageously, discardingregions that contain less pixels than the threshold value can filterdown the defect to ensure that only the most valuable defect regions areselected and move onto the next phase of detection. In a step 228, thedefect region can be colored according to the adjusted color image. Animage identification number can be used to identify the location of thedefect region on the surface in a step 230.

As shown in FIGS. 2A and 2B, the method 100 to locate a defect in ane-coat on a surface can include a step 240 of classifying the defect.The defect can be classified using a classifier algorithm to be sortedinto various defect types or can be classified as noise. Defectsclassified as noise can be rejected and discarded in a step 242. As anon-limiting example, the classifier can be a SVM or a deep learningcoevolution neural network, such as AlexNet. One of ordinary skill inthe art can select a classifier within the scope of the presentdisclosure.

The defect region can have classification information associated withit. Classification information can include, but is not limited to,shape, position, size and the probability of noise being identified.More specifically, the classification information can include colorinformation because different e-coat defects have different colors.Color information can be used to filter out noise because color istypically the same for the entire vehicle. Where classificationinformation has been obtained and the defect has been classified, theframe identification of the image can be used to locate the defect.

As shown in FIGS. 9-10 , the method 100 to locate a defect in an e-coaton a surface can include a step 260 of performing one of repairing thedefect or a step 270 of changing a parameter of an e-coating process toform a modified e-coating process and coating another surface using themodified e-coating process. The step of repairing the defect can includeusing a hit testing system to locate the physical location of the defecton the surface in a step 262. In a step 264, the hit test can use theidentification number of the image to find a calibrated position of thesurface and simulate the surface using a 3D CAD model of the surface.The hit testing can be used to trace a ray from the center pixel of thedefect region in a computer simulation until it intersects with a pointon the 3D CAD model of the surface. Hit testing for the defect regioncan be repeated at each corner of the defect region in a step 266.

The method 100 of the present disclosure can be repeated multiple timesas the surface moves along the conveyor in a step 280. As the surface ismoves along the conveyor and several images are taken, multiple imagesof the same defect can be captured, resulting in multiple defect regionsfor the same defect in a step 282. Where defect regions are repeated inmore than one image, the defect regions can have the same hit testlocation on the surface. In a step 284, the regions can be clusteredtogether are assumed to be the same defect and can be studied as a groupfor classification.

The step 270 of changing a parameter of the e-coating to form a modifiede-coating process can include qualifying the defect region in a step 272and using the data collected to measure and improve the painting processin a step 274. As a result, another surface can be e-coated using themodified e-coating process Advantageously, collecting this data canallow for the improvement of e-coating efficiency and accuracy and, as aresult, minimize the occurrence of defects. As a non-limiting example, aspray droplet size can be modified to optimize coverage. Further, aspray amount can be altered to minimize over application and/or underapplication.

The present disclosure also contemplates a system 300 for locating adefect in an e-coat on a surface described hereinabove. The system 300can include a surface 302 which can be present on a chassis 304. Thesystem 300 can include a camera 306 for acquiring an image and aconveyor 308 for moving the chassis 304 to a first location and a secondlocation. The system can calculate the distance the chassis can movealong the conveyor between each image being acquired. The system caninclude one of more lights to illuminate the surface and allow for abetter and more accurate image to be captured. The system can include agraphic processing unit (GPU) for locating a defect in an e-coat on asurface 302.

In certain embodiments, the system can be communicatively coupled to oneor more remote platforms. The communicative coupling can includecommunicative coupling through a networked environment. The networkedenvironment can be a radio access network, such as LTE or 5G, a localarea network (LAN), a wide area network (WAN) such as the Internet, orwireless LAN (WLAN), for example. It will be appreciated that this isnot intended to be limiting, and that the scope of this disclosureincludes implementations in which one or more computing platforms andremote platforms can be operatively linked via some other communicationcoupling. The one or more computing platforms can be configured tocommunicate with the networked environment via wireless or wiredconnections. In addition, in an embodiment, the one or more computingplatforms can be configured to communicate directly with each other viawireless or wired connections. Examples of one or more computingplatforms can include, but are not limited to, smartphones, wearabledevices, tablets, laptop computers, desktop computers, Internet ofThings (IoT) device, or other mobile or stationary devices. In certainembodiments, a system can be provided that can also include one or morehosts or servers, such as the one or more remote platforms connected tothe networked environment through wireless or wired connections.According to one embodiment, remote platforms can be implemented in orfunction as base stations (which can also be referred to as Node Bs orevolved Node Bs (eNBs)). In certain embodiments, remote platforms caninclude web servers, mail servers, application servers, etc. Accordingto certain embodiments, remote platforms can be standalone servers,networked servers, or an array of servers.

The system can include one or more processors for processing informationand executing instructions or operations, including such instructionsand/or operations stored on one or more non-transitory mediums. One ormore processors can be any type of general or specific purposeprocessor. In some cases, multiple processors can be utilized accordingto other embodiments. In fact, the one or more processors can includeone or more of general-purpose computers, special purpose computers,microprocessors, digital signal processors (DSPs), field-programmablegate arrays (FPGAs), application-specific integrated circuits (ASICs),and processors based on a multi-core processor architecture, asexamples. In some cases, the one or more processors can be remote fromthe one or more computing platforms. The one or more processors canperform functions associated with the operation of system which caninclude, for example, precoding of antenna gain/phase parameters,encoding and decoding of individual bits forming a communicationmessage, formatting of information, and overall control of the one ormore computing platforms, including processes related to management ofcommunication resources.

The system can further include or be coupled to a memory (internal orexternal), which can be coupled to one or more processors, for storinginformation and instructions that can be executed by one or moreprocessors, including any instructions and/or operations stored on oneor more non-transitory mediums. Memory can be one or more memories andof any type suitable to the local application environment, and can beimplemented using any suitable volatile or nonvolatile data storagetechnology such as a semiconductor-based memory device, a magneticmemory device and system, an optical memory device and system, fixedmemory, and removable memory. For example, memory can consist of anycombination of random access memory (RAM), read only memory (ROM),static storage such as a magnetic or optical disk, hard disk drive(HDD), or any other type of non-transitory machine or computer readablemedia. The instructions stored in memory can include programinstructions or computer program code that, when executed by one or moreprocessors, enable the one or more computing platforms to perform tasksas described herein.

In some embodiments, one or more computing platforms can also include orbe coupled to one or more antennas for transmitting and receivingsignals and/or data to and from one or more computing platforms. The oneor more antennas can be configured to communicate via, for example, aplurality of radio interfaces that can be coupled to the one or moreantennas. The radio interfaces can correspond to a plurality of radioaccess technologies including one or more of LTE, 5G, WLAN, Bluetooth,near field communication (NFC), radio frequency identifier (RFID),ultrawideband (UWB), and the like. The radio interface can includecomponents, such as filters, converters (for example, digital-to-analogconverters and the like), mappers, a Fast Fourier Transform (FFT)module, and the like, to generate symbols for a transmission via one ormore downlinks and to receive symbols (for example, via an uplink).

Example embodiments are provided so that this disclosure will bethorough, and will fully convey the scope to those who are skilled inthe art. Numerous specific details are set forth such as examples ofspecific components, devices, and methods, to provide a thoroughunderstanding of embodiments of the present disclosure. It will beapparent to those skilled in the art that specific details need not beemployed, that example embodiments may be embodied in many differentforms, and that neither should be construed to limit the scope of thedisclosure. In some example embodiments, well-known processes,well-known device structures, and well-known technologies are notdescribed in detail. Equivalent changes, modifications and variations ofsome embodiments, materials, compositions and methods can be made withinthe scope of the present technology, with substantially similar results.

What is claimed is:
 1. A method of locating a defect in an e-coat on asurface, comprising: acquiring an image of the surface; applying acorrection coefficient to the image to form an adjusted image, thecorrection coefficient relating pixel values of the image to acalibration value; separating a spectral component from the adjustedimage each of the plurality of pixels; modifying the spectral componentto create a modified spectral component, the modifying including a blockaverage determination; comparing the spectral component with themodified spectral component to form a difference image; dilating anderoding the difference image; identifying a region of interest from aone or more image regions using a blob detection threshold value tolocate the defect; classifying the defect; and performing one of:repairing the defect; and changing a parameter of an e-coating processto form a modified e-coating process and coating another surface usingthe modified e-coating process.
 2. The method of claim 1, wherein thestep of acquiring the image of the surface includes: placing the surfaceon a conveyor at a first location; moving the surface along the conveyorto a second location spaced away from the first location; tracking thesecond location with respect to the first location using a trackingdevice; calculating a distance between the first location and the secondlocation; illuminating the surface with a light; capturing the image ofthe surface at the second location; and categorizing the image at thesecond location, thereby allowing for the defect to be physicallylocated on the surface after it has been discovered and classified. 3.The method of claim 2, wherein the tracking device one of a rotaryencoder and a laser.
 4. The method of claim 2, wherein a distancebetween the first location and the second location is between 2 mm and 8mm.
 5. The method of claim 2, wherein a distance between the firstlocation and the second location is 4 mm.
 6. The method of claim 1,wherein acquiring the image of the surface uses a color digital camera.7. The method of claim 6, wherein the color digital camera has a Bayerpixel format.
 8. The method of claim 1, wherein the step of applying acorrection coefficient to the image to form an adjusted image, thecorrection coefficient relating pixel values of the image to acalibration value, includes: converting the image into a color imageformat, wherein the color image format has a red value, a blue value,and a green value assigned to each of a plurality of pixels of theimage; identifying the correction coefficient using a calibration plate;and applying the correction coefficient to each of the plurality ofpixels to form an adjusted image.
 9. The method of claim 8, wherein thecalibration value includes a plurality of calibration values.
 10. Themethod of claim 9, wherein the calibration plate includes an XRitepassport color calibration plate.
 11. The method of claim 10, whereinthe step of separating a spectral component from the adjusted imageincludes separating the adjusted image into multiple spectralcomponents.
 12. The method of claim 11, wherein the multiple spectralcomponents include: separating the adjusted image into a red monochrometo form a red image; separating the adjusted image into a bluemonochrome to form a blue image; separating the adjusted image into agreen monochrome to form a green image; and maximizing at least one ofthe red image, the blue image, and the green image at each one of theplurality of pixels to form a grey image.
 13. The method of claim 1,wherein the step of modifying the spectral component to create amodified spectral component, the modifying including a block averagedetermination, includes: selecting a block region made of pixels, theblock regions having a block height, a block width, and a percentilelimit; sorting the pixels of each block region by pixel intensity;discarding outlier pixels outside the percentile limit; averagingremaining pixels, whereby an average pixel intensity is determined; andreplacing the block region with the average pixel intensity to form abackground intensity image.
 14. The method of claim 1, wherein the stepof comparing the spectral component with the modified spectral componentto form a difference image, includes: comparing a background intensityimage and a grey image at each of the plurality of pixels to form adifference image; eroding the difference image to remove noise to forman eroded image; and merging the difference image and the eroded imageusing a baseline value to form a final eroded image.
 15. The method ofclaim 1, wherein the step of identifying a region of interest from theone or more image regions using a blob detection threshold value tolocate the defect, includes: saving pixels with an intensity in a finaleroded image greater than zero; bounding pixels using the thresholdvalue to form a defect region; coloring the defect region according tothe adjusted image; and using an image identification number to identifythe location of the defect region.
 16. The method of claim 15, whereinthe threshold value is five pixels.
 17. The method of claim 13, whereinthe step of comparing the spectral component with the modified spectralcomponent to form a difference image, includes: comparing a backgroundintensity image and the grey image at each of the plurality of pixels toform a difference image; eroding the difference image to remove noise toform an eroded image; and merging the difference image and the erodedimage using a baseline value to form a final eroded image.
 18. Themethod of claim 1, wherein the step of repairing the defect includesusing a hit testing system to locate the defect on the surface.
 19. Themethod of claim 18, wherein the hit testing system uses a calibratedposition of the surface.
 20. A system for locating a defect in an e-coaton a surface using the method of claim 1.